Medical device with acoustic sensor

ABSTRACT

In at least one example, a medical device is provided. The medical device includes at least one therapy electrode, at least one electrocardiogram (ECG) electrode, at least one acoustic sensor, and at least one processor coupled with the at least one acoustic sensor, the at least one ECG electrode, and the at least one therapy electrode. The at least one processor can receive at least one acoustic signal from the at least one acoustic sensor, receive at least one electrode signal from the ECG electrode, detect at least one unverified cardiopulmonary anomaly using the at least one electrode signal, and verify the at least one unverified cardiopulmonary anomaly with reference to data descriptive of the at least one acoustic signal.

RELATED APPLICATIONS

This application claims benefit under 35 U.S.C. §119(e) of U.S.Provisional Application Ser. No. 62/134,881, titled “MEDICAL DEVICE WITHACOUSTIC SENSOR,” filed Mar. 18, 2015, which is hereby incorporatedherein by reference in its entirety.

BACKGROUND

1. Technical Field

This disclosure relates to medical devices, and more particularly tomedical devices that monitor the cardiopulmonary system using one ormore acoustic sensors.

2. Discussion

Some conventional medical devices that monitor the cardiopulmonarysystem obtain a subject's electrocardiogram (ECG) signal from bodysurface electrodes. Determining the true characteristics of anambulatory subject's cardiac cycle based on an ECG signal in this mannercan be difficult. Electrical noise and electrode fall-off frequentlydegrade the quality of the ECG signal. In addition, the characteristicsof ECG signals vary from subject to subject due to factors such as thesubject's state of health and individual physiology.

Known ambulatory wearable defibrillators, such as the LifeVest® WearableCardioverter Defibrillator available from ZOLL Medical Corporation ofChelmsford, Mass., use four ECG sensing electrodes in a dual-channelconfiguration. That is, an electrical signal provided by one of the fourECG sensing electrodes is paired with the electrical signal provided byanother of the four ECG sensing electrodes to form a channel. Thisarrangement of ECG sensing electrodes is usually suitable because inmost cases it is rare that noise or electrode movement affects theentire body circumference. The dual-channel configuration providesredundancy and allows the system to operate on a single channel ifnecessary. Because signal quality also varies from subject to subject,having two channels provides the opportunity to have improved signalpickup, since the ECG sensing electrodes are located in different bodypositions.

SUMMARY

Some aspects and examples disclosed herein manifest an appreciation forthe sporadic inability of medical devices relying solely on conventionalECG sensing electrodes to determine the functional effect of acardiopulmonary anomaly. For example, medical devices in accord with atleast one example disclosed herein utilize a combination of electrodeand acoustic sensor data to detect cardiopulmonary anomalies withincreased accuracy and precision relative to conventional ECG sensingelectrode based systems. With this enhanced cardiopulmonary data,various medical devices disclosed herein are better able to discriminatebetween detected cardiopulmonary anomalies that substantially impaircardiac or pulmonary function and those that do not. Using the enhancedcardiopulmonary data, these medical devices may modify the manner inwhich treatment is provided to a subject. For example, the medicaldevices may treat those anomalies that substantially impaircardiopulmonary function and may defer treatment where cardiopulmonaryfunction is not substantially impaired.

In at least one example, a medical device is provided. The medicaldevice includes at least one therapy electrode, at least oneelectrocardiogram (ECG) electrode, at least one acoustic sensor, and atleast one processor coupled with the at least one acoustic sensor, theat least one ECG electrode, and the at least one therapy electrode. Theat least one processor can receive at least one acoustic signal from theat least one acoustic sensor, receive at least one electrode signal fromthe ECG electrode, detect at least one unverified cardiopulmonaryanomaly using the at least one electrode signal, and verify the at leastone unverified cardiopulmonary anomaly with reference to datadescriptive of the at least one acoustic signal.

In the medical device, the data descriptive of the acoustic signalincludes at least one of S1, S2, S3, S4, EMAT, % EMAT, SDI, and LVST.The unverified cardiopulmonary anomaly includes at least one ofsupraventricular tachycardia, ventricular tachycardia, pulselesselectrical activity, asystole, a heart murmur, sleep apnea, andrespiratory failure. The at least one processor can verify disconnectionof the at least one ECG electrode with reference to the data descriptiveof the at least one acoustic signal.

The medical device may further include at least one accelerometercoupled with the at least one processor, wherein the at least oneprocessor can further verify the at least one unverified cardiopulmonaryanomaly with reference to data descriptive of at least on motion signalfrom the at least one accelerometer. In the medical device, the at leastone processor can acquire and record the at least one acoustic signalduring a configurable period of time. The at least one processor canalso execute a sleep test in response to receiving input requesting thesleep test.

In another example, a medical device is provided. The medical deviceincludes at least one electrocardiogram (ECG) electrode, at least oneacoustic sensor, and at least one processor coupled with the at leastone acoustic sensor and the at least one ECG electrode and configured toreceive at least one acoustic signal from the at least one acousticsensor, to receive at least one electrical signal from the at least oneECG electrode, and to detect at least one cardiopulmonary anomaly usingthe at least one electrical signal and the at least one acoustic signal.The medical device may include an ambulatory medical device.

In the medical device, the at least one processor may be configured todetect the at least one cardiopulmonary anomaly at least in part byidentifying the at least one cardiopulmonary anomaly using the at leastone electrical signal and verifying the at least one cardiopulmonaryanomaly using the at least one acoustic signal. The medical device mayfurther include a garment. The at least one ECG electrode and the atleast one acoustic sensor may be integrated with the garment. Themedical device may further include at least one therapy electrodecoupled with the at least one processor and integrated with the garment.The at least one acoustic signal may include at least one of S1, S2, S3,and S4. The at least one processor may be configured to detect the atleast one cardiopulmonary anomaly at least in part by calculating, basedon the at least one acoustic signal, at least one of electromechanicalactivation time (EMAT), EMAT as a percentage of cardiac cycle time,systolic dysfunction index, and left ventricular systolic time. The atleast one cardiopulmonary anomaly may include at least one ofsupraventricular tachycardia, ventricular tachycardia, pulselesselectrical activity, asystole, a heart murmur, sleep apnea, andrespiratory failure.

In the medical device, the at least one processor may be configured todetect disconnection of the at least one ECG electrode based on the atleast one acoustic signal. The medical device may further include atleast one motion sensor coupled with the at least one processor. The atleast processor may be configured to receive at least one motion signalfrom the at least one motion sensor and to detect the at least onecardiopulmonary anomaly using the at least one electrical signal, the atleast one acoustic signal, and the at least one motion signal.

In the medical device, the at least one acoustic sensor may include atleast one motion sensor. The at least one acoustic sensor may include atleast one accelerometer. The at least processor may be configured toreceive at least one signal from the at least one accelerometer and topartition the at least one signal into the at least one acoustic signaland at least one motion signal. The at least one motion signal mayinclude frequencies less than approximately 10 hertz. The at least oneacoustic signal may include frequencies greater than approximately 10hertz. The at least one processor may be configured to partition the atleast one signal into a first frequency band for subject motion, asecond frequency band for chest compression motion and/or sounds, athird frequency band for heart sounds, and a fourth frequency band forbreath sounds. The first frequency band may include frequencies lessthan approximately 6 hertz, the second frequency band may includefrequencies between approximately 6 and 20 hertz, the third frequencyband may include frequencies between approximately 20 hertz and 150hertz, and the fourth frequency band may include frequencies betweenapproximately 300 to 1200 hertz.

In another example, a method of monitoring a subject using a wearabledefibrillator is provided. The wearable defibrillator includes at leastone electrocardiogram (ECG) electrode and at least one acoustic sensor.The method comprises acts of receiving at least one acoustic signal fromthe at least one acoustic sensor, receiving at least one electricalsignal from the at least one ECG electrode, and detecting at least onecardiopulmonary anomaly using the at least one electrical signal and theat least one acoustic signal.

In the method, the act of receiving the at least one acoustic signal mayinclude an act of receiving at least one of S1, S2, S3, and S4. The actof detecting the at least one cardiopulmonary anomaly may include an actof calculating, based on the at least one acoustic signal, at least oneof electromechanical activation time (EMAT), EMAT as a percentage ofcardiac cycle time, systolic dysfunction index, and left ventricularsystolic time. The act of detecting the at least one cardiopulmonaryanomaly may include an act of detecting at least one of supraventriculartachycardia, ventricular tachycardia, pulseless electrical activity,asystole, a heart murmur, sleep apnea, and respiratory failure.

The medical device may further include at least one motion sensor. Themethod may further include acts of receiving at least one motion signalfrom the at least one motion sensor and detecting the at least onecardiopulmonary anomaly using the at least one electrical signal, the atleast one acoustic signal, and the at least one motion signal.

In another example, a medical device is provided. The medical deviceincludes a memory, at least one accelerometer, and at least oneprocessor coupled with the at least one accelerometer and the memory.The at least one processor is configured to receive at least oneacoustic signal from the at least one accelerometer, to receive at leastone motion signal from the at least one accelerometer, and to execute asleep test configured to store data descriptive of the at least oneacoustic signal and the at least one motion signal in the memory.

The medical device may further include comprising at least one ECGelectrode coupled with the at least one processor. The at least oneprocessor may be configured to receive at least one electrical signalfrom the at least one ECG electrode and the sleep test is configured tostore data descriptive of the at least one electrical signal in thememory. The medical device may further include a garment. The at leastone ECG electrode and the at least one accelerometer may be integratedwith the garment. The medical device may further include at least onetherapy electrode coupled with the at least one processor and integratedwith the garment. The at least one processor may be configured toexecute the sleep test at a configurable time. The medical device mayfurther include at least one motion sensor distinct from theaccelerometer and coupled with the at least one processor. The at leastone motion sensor may be positioned on a wrist of a subject. The atleast one processor may be configured to receive one or more motionsignals from the at least one motion sensor. The sleep test may beconfigured to store data descriptive of the one or more motion signalsin the memory.

Still other aspects and advantages of the examples disclosed herein arediscussed in detail below. Moreover, it is to be understood that boththe foregoing information and the following detailed description aremerely illustrative examples of various aspects, and are intended toprovide an overview or framework for understanding the nature andcharacter of the claimed subject matter. Any example disclosed hereinmay be combined with any other example. References to “an example,”“some examples,” “an alternate example,” “various examples,” “oneexample,” “at least one example,” “this and other examples” or the likeare not necessarily mutually exclusive and are intended to indicate thata particular feature, structure, or characteristic described inconnection with the example may be included in at least one example. Theappearances of such terms herein are not necessarily all referring tothe same example.

Furthermore, in the event of inconsistent usages of terms between thisdocument and documents incorporated herein by reference, the term usagein the incorporated references is supplementary to that of thisdocument; for irreconcilable inconsistencies, the term usage in thisdocument controls. In addition, the accompanying drawings are includedto provide illustration and a further understanding of the variousaspects and examples, and are incorporated in and constitute a part ofthis specification. The drawings, together with the remainder of thespecification, serve to explain principles and operations of thedescribed and claimed aspects and examples.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, components that are identical or nearly identical may berepresented by a like numeral. For purposes of clarity, not everycomponent is labeled in every drawing. In the drawings:

FIG. 1 is a functional schematic one example of a pacing device;

FIG. 2 is an illustration of one example of an ambulatory medicaldevice;

FIGS. 3A-B are illustrations of one example of a medical devicecontroller for an ambulatory medical device;

FIG. 4 is an illustration of one example of an external medical device;

FIG. 5 is a flow diagram of one example of a process analyzing enhancedcardiopulmonary data; and

FIG. 6 is an illustration of potential locations foraccelerometers/acoustic sensors on the body of a subject.

DETAILED DESCRIPTION

Medical devices in accord with various examples disclosed herein utilizeenhanced cardiopulmonary data to implement a wide variety offunctionality. For instance, according to some examples, a medicaldevice includes a cardiopulmonary function analyzer configured toprocess enhanced cardiopulmonary data, which may include acoustic andelectrode signals among other signals, to identify cardiopulmonaryanomalies that are difficult to detect using conventional ECG sensingelectrodes. Examples of these cardiopulmonary anomalies include superventricular tachycardia (SVT) as distinct from ventricular tachycardia(VT), asystole, ECG sensing electrode falloff, pulseless electricalactivity (PEA), asystole, pseudo-PEA, bradycardia, heart murmurs, sleepapnea, and respiratory arrest.

In some examples, the cardiopulmonary function analyzer also determineshow to address the identified cardiopulmonary anomaly. For example,where the medical device is a monitor, the cardiopulmonary functionanalyzer may issue an alarm describing the anomaly to an externalentity, such as a user or a computer system distinct from the medicaldevice. Where the medical device is a defibrillator, the cardiopulmonaryfunction analyzer may address the anomaly by delivering a defibrillatingshock to a subject. Where the medical device is a pacing device, thecardiopulmonary function analyzer may address the anomaly by deliveringone or more pacing pulses.

Any of the medical devices disclosed herein may be non-invasive,bodily-attached, or ambulatory. As used herein, the term non-invasivemeans that the device does not penetrate the body of a subject. This isin contrast to invasive devices, such as implantable medical devices, inwhich at least a portion of the device is disposed subcutaneously. Theterm bodily-attached means that at least a portion of the device (otherthan its electrodes in the case of a defibrillator, cardioverter, orpacer) is removably attached to the body of a subject, such as bymechanical coupling (e.g., by a wrist strap, cervical collar, bicepring), adhesion (e.g., by an adhesive gel intermediary), suction,magnetism, fabric or other flexible material (e.g., by straps orintegration into a garment) or other body mounting features not limitedby the aforementioned examples. These coupling elements hold the devicein a substantially fixed position with respect to the body of thesubject. The term ambulatory means that the device is capable of anddesigned for moving with the subject as the subject goes about theirdaily routine.

The examples of the methods and apparatuses discussed herein are notlimited in application to the details of construction and thearrangement of components set forth in the following description orillustrated in the accompanying drawings. The methods and apparatusesare capable of implementation in other examples and of being practicedor of being carried out in various ways. Examples of specificimplementations are provided herein for illustrative purposes only andare not intended to be limiting. In particular, acts, elements andfeatures discussed in connection with any one or more examples are notintended to be excluded from a similar role in any other examples.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. Any references toexamples or elements or acts of the systems and methods herein referredto in the singular may also embrace examples including a plurality ofthese elements, and any references in plural to any example or elementor act herein may also embrace examples including only a single element.References in the singular or plural form are not intended to limit thepresently disclosed systems or methods, their components, acts, orelements. The use herein of “including,” “comprising,” “having,”“containing,” “involving,” and variations thereof is meant to encompassthe items listed thereafter and equivalents thereof as well asadditional items. References to “or” may be construed as inclusive sothat any terms described using “or” may indicate any of a single, morethan one, and all of the described terms.

Medical Device

Various examples disclosed herein utilize enhanced cardiopulmonary data(e.g., acoustic data and electrode data) to detect and treatcardiopulmonary anomalies, such as SVT as distinct from VT, asystole,ECG sensing electrode falloff, PEA, pseudo-PEA, bradycardia, heartmurmurs, sleep apnea, and respiratory arrest. FIG. 1 illustrates amedical device 100 in accord with some examples. As shown in FIG. 1, themedical device 100 includes a medical device controller 102, one or moreaccelerometers/acoustic sensors 122, and one or more electrodes 120. Asillustrated in FIG. 1, the medical device controller 102 includes aprocessor 118, a sensor interface 114, a cardiopulmonary functionanalyzer 112, a therapy delivery interface 116, data storage 104, anetwork interface 106, a user interface 108, and a battery 110. Theprocessor 118 can be implemented using a variety of commerciallyavailable processors. Specific examples of the processor 118 aredescribed further below. The data storage 104 includes cardiopulmonarydata 117. The sensor interface 114 can include an acoustic signalprocessing component 124, an electrode signal processing component 126,and motion signal processing component 128. In some implementations,components such as cardiopulmonary function analyzer 112 and sensorinterface 114 can be implemented within processor 118. In someimplementations, the components can be implemented in circuitry that isseparate from processor 118.

The medical device 100 may be any of a variety of medical devicesincluding defibrillators, monitors, CPR systems, pacing devices, andother medical devices. More specific examples of medical devices inaccord with the medical device 100 are described further below withreference to FIGS. 2-4.

As shown in FIG. 1, the acoustic signal processing component 124 iscoupled to and receives acoustic signals from the accelerometer/acousticsensor 122. Similarly, the electrode signal processing component 126 iscoupled to and receives electrode signals from the electrode 120. Theelectrode 120 may comprise any of a variety of commercially availableelectrodes, some examples of which are described further below.

Likewise, the motion signal processing component 128 is coupled to andreceives motion signals from the accelerometer/acoustic sensor 122. Asillustrated in FIG. 1, in some examples, the cardiopulmonary functionanalyzer 112 can be coupled with (via the processor 118) and receiveprocessed acoustic data from the acoustic signal processing component124, processed electrode data from the electrode signal processingcomponent 126, and processed motion data from the motion signalprocessing component 128. Examples of processes executed by the acousticsignal processing component 124, the electrode signal processingcomponent 126, and the motion signal processing component 128 aredescribed further below with reference to FIG. 5.

According to one example illustrated by FIG. 1, the cardiopulmonaryfunction analyzer 112 is configured to detect heart beats, breathsounds, and cardiopulmonary anomalies and determine whether the detectedanomalies substantially impair cardiac or pulmonary function. Forexample, the cardiopulmonary function analyzer 112 can be implementedusing a variety of hardware or software components. When executingaccording to this configuration, in some examples, the cardiopulmonaryfunction analyzer 112 detects cardiopulmonary anomalies by scanningprocessed acoustic data and processed electrode data for patternsindicative of cardiopulmonary anomalies. Responsive to identifying adata pattern indicative of a cardiopulmonary anomaly, thecardiopulmonary function analyzer 112 identifies a routine to addressthe cardiopulmonary anomaly based on the identity of the anomaly and aconfidence that the anomaly actually exists. Next, the cardiopulmonaryfunction analyzer 112 initiates the identified routine. The datapatterns scanned for by the cardiopulmonary function analyzer 112 areindicative of a wide variety of cardiopulmonary anomalies. Examples ofthese anomalies include cardiac arrhythmias (e.g., bradycardia,tachycardia, SVT, an irregular cardiac rhythm, PEA, and asystole),murmurs, sleep apnea, and respiratory arrest. The data patterns may alsoindicate problems with the medical device itself such as faulty ordisconnected sensors. Specific examples of the data patterns scanned forby the cardiopulmonary function analyzer 112 and actions takenresponsive to detection of the data patterns are described further belowwith reference to FIG. 5.

In some examples, the cardiopulmonary function analyzer 112 isconfigured to leverage the differing originating modalities of theprocessed acoustic data and electrode data to advantageous effect. Forexample, data patterns that indicate VT are sometimes present inprocessed electrode data where the subject is actually experiencing SVT.Accordingly, in an implementation, the cardiopulmonary function analyzer112 can analyze the processed acoustic data to determine whether VTindicated by the processed electrode data actually exists. If thecardiopulmonary function analyzer 112 determines based on the processedacoustic data that no VT condition exists, the cardiopulmonary functionanalyzer 112 may not initiate treatment as doing so may convert SVT intoVT, which poses a greater risk to the health of the subject than SVT. Insome examples, as described further below with reference to FIG. 5, thecardiopulmonary function analyzer 112 verifies that no VT conditionexists by comparing the intensity and variability of S1 to predefinedthresholds. S1 is a heart sound described further below.

Similarly, in some examples, the cardiopulmonary function analyzer 112can analyze processed acoustic data to determine whether the heart of asubject is mechanically pumping blood where the processed electrode dataindicates asystole or PEA. If sufficient blood is being pumped (e.g., ifthe ejection fraction exceeds a predefined threshold), thecardiopulmonary function analyzer 112 may defer treatment. In someexamples, as described further below with reference to FIG. 5, thecardiopulmonary function analyzer 112 determines whether the ejectionfraction exceeds the predefined threshold by comparing EMAT to apredefined threshold. EMAT is a metric calculated based on heart soundsand is described further below.

In some examples, the cardiopulmonary function analyzer 112 can analyzeprocessed acoustic data that covers periods of time where electrode datais not available (e.g., due to an enforced blanking interval, temporaryelectrode saturation, or electrode fall off) and takes appropriateaction based on the condition of a subject as indicated by the processedacoustic data.

In some implementations, the cardiopulmonary function analyzer 112 cananalyze processed acoustic data and processed motion data received viamultiple signals generated by the accelerometer/acoustic sensor 122. Inthis regard, the sensor 122 can provide the signals over one or morechannels to maintain signal separation. In some implementations, themultiple signals can be combined or multiplexed within a signal channel.For example, the cardiopulmonary function analyzer 112 may analyze datarepresentative of a subject's respiration encoded from a first channel,data representative of a subject's heart sounds encoded from a secondchannel, and data representative of a subject's position encoded from athird channel.

One example of a cardiopulmonary anomaly detection process that thecardiopulmonary function analyzer 112 is configured to execute isdescribed further below with reference to FIG. 5.

In some implementations, the cardiopulmonary function analyzer 112 canbe configured to execute an at-home sleep test. When executing accordingto this configuration, the cardiopulmonary function analyzer 112 mayreceive information indicating that a user has requested execution ofthe sleep test via a user interface of the medical device, such as theuser interface 108. Responsive to receipt of this request information,the cardiopulmonary function analyzer 112 can record and store enhancedcardiopulmonary data for a configurable period of time (e.g., 6-10hours) specified in the request information or within a discreteconfigurable parameter of the medical device. This period of time may,for example, represent time when the subject is expected to be asleep.Enhanced cardiopulmonary data can include information based on one ormore of the processed signals from the sensor interface 114 and caninclude motion data, acoustic data, electrode data, or aggregated datagenerated by processing other data. In some examples, the configurableperiod of time can include a future time period (e.g., 2 days from thecurrent day) or a reoccurring period (e.g., every other Tuesday for thenext month). Upon completion of each sleep test recording, thecardiopulmonary function analyzer 112 may transmit the recorded data toa remote system via, for example, a network interface, such as thenetwork interface 106.

In some examples, the medical device 100 can generate a variety ofreports and metrics based on the data analyzed by the cardiopulmonaryfunction analyzer 112. For example, the medical device 100 can cause areport to be presented to a user via the user interface 108. In somecases, the medical device 100 can cause a report (and any associatedanalytic metrics) to be transmitted to a network output device (e.g., anetwork printer on a same or different network as the medical device100). In some instances, the report can be transmitted to a server onsame or different network (e.g. at a remote location) for furtherprocessing and storage.

In an example, the cardiopulmonary function analyzer 112 may generateboth summary and full disclosure reports in portable document format foreach recorded period. The cardiopulmonary function analyzer 112 may saveor print these reports in response to a request for the same. The fulldisclosure report that depicts all enhanced cardiopulmonary data (e.g.,electrode data and acoustic data) for each recorded period. The summaryreport summaries the information presented in the full disclosurereport.

In some examples, the variety of reports include one or more reportsthat present information descriptive of the hemodynamic performance ofthe heart and respiratory effort of a subject. For instance, a reportmay provide information descriptive of chest rise and fall duringrespiration as determined from acoustic vibration of the chest wallduring respiration. These reports may also display informationdescriptive of the restlessness of a subject, as determined fromprocessed motion data. This processed motion data may be generated byone or more accelerometers positioned on the torso and wrist of asubject, such as within a pocket, belt, or watch worn by the subject andin communication with the cardiopulmonary function analyzer 112 via aBAN. For example, such data may provide insights into a subject's bodyposition during sleep or limb movements and position. The processedmotion data may also be generated by an internal 3-axis gyroscopeincluded in some examples.

In some examples, the cardiopulmonary function analyzer 112 reads valuesof one or more configurable parameters that specify targeted operationalcharacteristics of the cardiopulmonary function analyzer 112. Theseoperational parameters may include upload filter criteria that specifiesthe data type and frequency with which the cardiopulmonary functionanalyzer 112 transmits enhanced cardiopulmonary data to a remotecomputer, such as the centralized server described further below withreference to FIG. 2 via the network interface 106.

In some examples illustrated by FIG. 1, the acoustic signal processingcomponent 124 is configured to detect and record a variety of soundsrelated to cardiopulmonary function. To process analog and digitalacoustic signals received from the accelerometer/acoustic sensor 122,the acoustic signal processing component 124 may include variouscircuitry, such as amplifiers, filters, transducers, analog to digitalconverters, analog signal processors, and digital signal processors. Inat least one example, the acoustic signal processing component 124processes signals received from one or more acoustic channels. Furtheraccording to some examples, the acoustic signal processing component 124transmits processed acoustic data descriptive of the acoustic signals tothe cardiopulmonary function analyzer 112 for subsequent analysis. Insome examples, the processor 118 exchanges data with the sensorinterface 114 and executes the cardiopulmonary function analyzer 112.Additional aspects and functions of the processor 118 are describedfurther below.

In healthy adults, there are at least two normal heart sounds, commonlyreferred to as S1 and S2. A third heart sound, commonly referred to asS3 (also called a protodiastolic gallop or ventricular gallop), may beindicative of a problem with a subject's heart when present. Forexample, in subjects over 40 years old, S3 has been associated with anabnormal diastolic filling pattern. The presence of S3 may signalcardiac problems like a failing left ventricle as in dilated congestiveheart failure. A fourth heart sound, commonly referred to as S4 (alsocalled a presystolic gallop or atrial gallop), is indicative of aproblem with a subject's heart when present. For example, S4 is oftenassociated with an increased left ventricular stiffness. Heart murmursmay also be present in some subjects and may indicate cardiac problems.

In some examples, the acoustic signal processing component 124 isconfigured to detect and record heart sound values including any one orall of S1, S2, S3, and S4. Other heart sound values which may bemonitored and recorded by the acoustic signal processing component 124may include any one or more of electromechanical activation time (EMAT),percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and leftventricular systolic time (LVST).

EMAT is generally measured from the onset of the Qwave on the ECG to theclosure of the mitral valve within the S1 heart sound. Prolonged EMAThas been associated with reduced left ventricular ejection fraction (LVEF, being a measure of how much blood is being pumped out of the leftventricle of the heart with each contraction).

% EMAT is EMAT divided by a dominant RR interval. In this regard, % EMATis related to an efficiency of the pump function of the heart.

SDI is a multiplicative combination of ECG and heart sound values (EMA,S3, QRS duration and QR interval). SDI can be used to predict leftventricular systolic dysfunction.

LVST is the systolic portion of the cardiac cycle and is defined as thetime interval between the S1 and the S2 heart sounds. LVST may have someheart rate dependence, and can be approximately 40% (range 30-50%) ofthe cardiac cycle. LVST can be affected by disease that produces poorcontractility or a low ejection fraction.

In some examples, during an initial fitting of the medical device, themedical device may record enhanced cardiopulmonary data to use as abaseline for an individual subject. For example, a baseline EMAT may berecorded and later used to analyze degree of degradation occurringwithin the cardiopulmonary function of the subject. In one example, thebaseline EMAT and baseline ejection fraction can be recorded andsubsequent EMAT values can be used to approximate subsequent ejectionfractions.

In some examples, the recorded heart sound values described above may bestored in non-volatile data storage. For example, in one example thesevalues are stored as cardiopulmonary data 117 within the data storage104. In at least one example, the cardiopulmonary function analyzer 112periodically monitors the recorded heart sound values for events ofinterest. In this example, where the recorded heart sound valuesindicate a potential health risk, the cardiopulmonary function analyzer112 may notify an external entity of the risk. For example, where anEMAT value degrades gradually over a specified period of time, thecardiopulmonary function analyzer 112 may transmit a communication to ahealthcare provider indicating this degradation.

In other examples, when executing according to its configuration, theacoustic signal processing component 108 identifies and recordsbreathing sounds such as those that accompany normal respiration,snoring, episodes of sleep apnea, and respiratory arrest.

In some examples, the acoustic signal processing component 124 readsvalues of one or more configurable parameters that specify targetedoperational characteristics of the acoustic signal processing component124 or the accelerometer/acoustic sensor 122. These operationalparameters may specify the sampling rate, filter coefficients, recordingduration and interval, and noise thresholds, used to process acousticdata. The value of the recording duration and interval operationalparameter specifies the length of time of each acoustic recording andthe interval of time between recordings. In one example, the value ofthe recording duration and interval operational parameter specifies aduration of 5 seconds to be recorded every 10 minutes. In some examples,the acoustic signal processing component 124 and theaccelerometer/acoustic sensor 122 continuously record, process, andstore processed acoustic data.

In one example illustrated by FIG. 1, the electrode signal processingcomponent 126 is configured to detect and record cardiac activity of asubject. For example, when executing according to this configuration,the electrode signal processing component 126 may detect and record ECGsignals. Further according to this example, the electrode signalprocessing component 126 transmits information descriptive of the ECGsignals to the cardiopulmonary function analyzer 112 for subsequentanalysis. As described above, the processed data from the electrodesignal processing component 126 can be monitored for cardiopulmonaryanomalies either alone or in combination with processed data from theacoustic signal processing component 124 or the motion signal processingcomponent 128.

In one example illustrated by FIG. 1, the motion signal processingcomponent 128 is configured to detect and record motion and physicalmovement of a subject. For example, when executing according to thisconfiguration, the motion signal processing component 128 may detect andrecord physical movement such as walking, falling, breath motion (e.g.,chest rising and falling), and the like. Further according to thisexample, the motion signal processing component 128 transmitsinformation descriptive of the accelerometer signals to thecardiopulmonary function analyzer 112 for subsequent analysis.

In one example illustrated by FIG. 1, the accelerometer/acoustic sensor122 may comprise any device that may detect sounds from a subject'scardiopulmonary system and provide an output signal responsive to thedetected heart and breath sounds. In some examples theaccelerometer/acoustic sensor 122 comprises a microphone or anaccelerometer. In some examples, the accelerometer comprises amicroelectromechanical system (MEMS) accelerometer or a multi-channelaccelerometer, for example, a three channel accelerometer. The acousticsensor may comprise a three channel accelerometer configured to sensemovement in each of three orthogonal axes. An example accelerometerwhich may be utilized in some examples is a LIS344ALH accelerometer,available from STMicroelectronics.

The accelerometer/acoustic sensor 122 and associated electronics may beconfigured to monitor any one or more of a subject's respiration, asubject's heart sounds, a subject's position, and an activity level of asubject. The accelerometer/acoustic sensor 122 and associatedelectronics may additionally or alternatively be configured to monitorother sounds which may be indicative of a state of health of a subject,for example, gastrointestinal sounds or the sounds of snoring or theabsence of such sounds to, for example, provide an indication of thesubject experiencing sleep apnea or respiratory arrest. Theaccelerometer/acoustic sensor 122 may provide signals indicative of thesubject's heart sounds on a first channel, signals indicative of thesubject's position on a second channel, and signals indicative of thesubject's activity level on a third channel. In some examples, thedifferent channels may be utilized to provide signals indicative of morethan one physiological characteristic or other characteristicsassociated with the state of the subject. For example, theaccelerometer/acoustic sensor 122 may provide signals indicative of thesubject's heart sounds on a first channel, signals indicative of thesubject's respiration on a second channel, and signals indicative of thesubject's body position on any or all of the first, second, and a thirdchannel. It should be appreciated that dependent on the underlyingcharacteristic that is being monitored, multiple signals related to thecharacteristic being monitored may be received over a single channel ora number of different channels.

In one example illustrated by FIG. 1, the electrode 120 may include anytype of sensing electrode, such as one or more ECG sensing electrodes asdescribed further below with reference to FIGS. 2 and 4.

In some examples in accord with FIG. 1, the battery 110 is arechargeable 3 cell 2200 mAh lithium ion battery pack that provideselectrical power to the other device components with a minimum 24 hourruntime between charges. It is appreciated that the battery capacity,runtime, and type (e.g., lithium ion, nickel-cadmium, or nickel-metalhydride) may be changed to best fit the specific application of themedical device controller 102.

According to the example illustrated in FIG. 1, the processor 118 iscoupled to the sensor interface 114, the therapy delivery interface 116,the data storage 104, the network interface 106, and the user interface108. The processor 118 performs a series of instructions that result inmanipulated data which are stored in and retrieved from the data storage104. According to a variety of examples, the processor 118 is acommercially available processor such as a processor manufactured byTexas Instruments, Intel, AMD, Sun, IBM, Motorola, Freescale, and ARMHoldings. However, the processor 118 may be any type of processor,multiprocessor or controller, whether commercially available orspecially manufactured. For instance, according to one example, theprocessor 118 may include a power conserving processor arrangement asdescribed in U.S. Pat. No. 8,904,214, titled SYSTEM AND METHOD FORCONSERVING POWER IN A MEDICAL DEVICE, issued Dec. 2, 2014, which ishereby incorporated herein by reference in its entirety. In one example,the processor 118 is an Intel® PXA270.

In addition, in several examples the processor 118 is configured toexecute a conventional real-time operating system (RTOS), such asRTLinux. In these examples, the RTOS may provide platform services toapplication software, such as some examples of the cardiopulmonaryfunction analyzer 112. These platform services may include inter-processand network communication, file system management and standard databasemanipulation. One or more of many operating systems may be used, andexamples are not limited to any particular operating system or operatingsystem characteristic. For instance, in some examples, the processor 118may be configured to execute a non-real time operating system, such asBSD or GNU/Linux.

As illustrated in FIG. 1, the cardiopulmonary function analyzer 112, theacoustic signal processing component 124, the electrode signalprocessing component 126, and the motion signal processing component 128may be implemented using hardware or a combination of hardware andsoftware. For instance, in one example, the cardiopulmonary functionanalyzer 112, the acoustic signal processing component 124, theelectrode signal processing component 126, and the motion signalprocessing component 128 are implemented as software components that arestored within the data storage 104 and executed by the processor 118. Inthis example, the instructions included in the cardiopulmonary functionanalyzer 112, the acoustic signal processing component 124, theelectrode signal processing component 126, and the motion signalprocessing component 128 program the processor 118 to analyze thecardiopulmonary function of a subject. In some examples, cardiopulmonaryfunction analyzer 112, the acoustic signal processing component 124, theelectrode signal processing component 126, and the motion signalprocessing component 128 may be application-specific integrated circuits(ASICs) that are coupled to the processor 118 and tailored to analyzethe cardiopulmonary function of a subject. Thus, examples of thecardiopulmonary function analyzer 112, the acoustic signal processingcomponent 124, the electrode signal processing component 126, and themotion signal processing component 128 are not limited to a particularhardware or software implementation.

In some examples, the components disclosed herein, such as thecardiopulmonary function analyzer 112, the acoustic signal processingcomponent 124, the electrode signal processing component 126, and themotion signal processing component 128 may read parameters that affectthe functions performed by the components. These parameters may bephysically stored in any form of suitable memory including volatilememory, such as RAM, or nonvolatile memory, such as a flash memory ormagnetic hard drive. In addition, the parameters may be logically storedin a propriety data structure, such as a database or file defined by auser mode application, or in a commonly shared data structure, such asan application registry that is defined by an operating system. Inaddition, some examples provide for both system and user interfaces, asmay be implemented using the user interface 108, that allow externalentities to modify the parameters and thereby configure the behavior ofthe components.

The data storage 104 includes a computer readable and writeablenonvolatile data storage medium configured to store non-transitoryinstructions and data. In addition, the data storage 104 includesprocessor memory that stores data during operation of the processor 118.In some examples, the processor memory includes a relatively highperformance, volatile, random access memory such as dynamic randomaccess memory (DRAM), static memory (SRAM) or synchronous DRAM. However,the processor memory may include any device for storing data, such as anon-volatile memory, with sufficient throughput and storage capacity tosupport the functions described herein. According to several examples,the processor 118 causes data to be read from the nonvolatile datastorage medium into the processor memory prior to processing the data.In these examples, the processor 118 copies the data from the processormemory to the non-volatile storage medium after processing is complete.A variety of components may manage data movement between thenon-volatile storage medium and the processor memory and examples arenot limited to particular data management components. Further, examplesare not limited to a particular memory, memory system, or data storagesystem.

The instructions stored on the data storage 104 may include executableprograms or other code that can be executed by the processor 118. Theinstructions may be persistently stored as encoded signals, and theinstructions may cause the processor 118 to perform the functionsdescribed herein. The data storage 104 also may include information thatis recorded, on or in, the medium, and this information may be processedby the processor 118 during execution of instructions. The medium may,for example, be optical disk, magnetic disk or flash memory, amongothers, and may be permanently affixed to, or removable from, themedical device controller 102.

In some examples, the cardiopulmonary data 117 includes cardiopulmonarydata detected, identified, and stored by the cardiopulmonary functionanalyzer 112. More particularly, according to the illustrated example,the cardiopulmonary data 117 includes information descriptive of cardiacfunction and respiratory function. For example, the cardiopulmonary data117 may include data such as ECG signal data, interpretations of the ECGsignal data (e.g., heartbeats), analog heart sounds, analog breathsounds, analog motion data, acoustic signals, electrode signals, motionsignals, processed motion data, processed acoustic data, and processedelectrode data.

As illustrated in FIG. 1, the cardiopulmonary function analyzer 112 andthe cardiopulmonary data 117 are separate components. However, in someexamples, the cardiopulmonary function analyzer 112 and thecardiopulmonary data 117 may be combined into a single component orre-organized so that a portion of the data included in thecardiopulmonary function analyzer 112, such as executable code thatcauses the processor 118 to analyze enhanced cardiopulmonary data,resides in the cardiopulmonary data 117, or vice versa. Such variationsin these and the other components illustrated in FIG. 1 are intended tobe within the scope of the examples disclosed herein.

The cardiopulmonary data 117 may be stored in any logical constructioncapable of storing information on a computer readable medium including,among other structures, flat files, indexed files, hierarchicaldatabases, relational databases or object oriented databases. These datastructures may be specifically configured to conserve storage space orincrease data exchange performance. In addition, various examplesorganize the cardiopulmonary data 117 into particularized and, in somecases, unique structures to perform the functions disclosed herein. Inthese examples, the data structures are sized and arranged to storevalues for particular types of data, such as integers, floating pointnumbers, character strings, arrays, linked lists, and the like.

As shown in FIG. 1, the medical device controller 102 includes severalsystem interface components 116, 106, and 114. Each of these systeminterface components is configured to exchange, i.e. send or receive,data with one or more specialized devices that may be located within thehousing of the medical device controller 102 or elsewhere. Thecomponents used by the interfaces 116, 106, and 114 may include hardwarecomponents, software components, or a combination of both. Within eachinterface, these components physically and logically couple the medicaldevice controller 102 to the specialized devices. This physical andlogical coupling enables the medical device controller 102 tocommunicate with and, in some examples, power or control the operationof the specialized devices. These specialized devices may includephysiological sensors, therapy delivery devices, and computer networkingdevices.

According to various examples, the hardware and software components ofthe interfaces 116, 106, and 114 implement a variety of coupling andcommunication techniques. In some examples, the interfaces 116, 106, and114 use leads, cables or other wired connectors as conduits to exchangedata between the medical device controller 102 and specialized devices.In some examples, the interfaces 116, 106, and 114 communicate withspecialized devices using wireless technologies such as radio frequency,infrared technology, and body area network (BAN) technology. Thesoftware components included in the interfaces 116, 106, and 114 enablethe processor 118 to communicate with specialized devices. Thesesoftware components may include elements such as objects, executablecode, and populated data structures. Together, these software componentsprovide software interfaces through which the processor 118 can exchangeinformation with specialized devices. Moreover, in at least someexamples where one or more specialized devices communicate using analogsignals, the interfaces 116, 106, and 114 further include componentsconfigured to convert analog information into digital information, andvice versa, to enable the processor 118 to communicate with specializeddevices.

As discussed above, the system interface components 116, 106, and 114shown in FIG. 1 support different types of specialized devices. Forinstance, the components of the sensor interface 114 couple theprocessor 118 to one or more physiological sensors such as a bodytemperature sensors, respiration monitors, and ECG sensing electrodes,one or more environmental sensors such as atmospheric thermometers,airflow sensors, video sensors, audio sensors, accelerometers, GPSlocators, and hygrometers. In these examples, the sensors may includesensors with a relatively low sampling rate, such as wireless sensors.Additionally, in at least one example, the acoustic signal processingcomponent 124, the electrode signal processing component 126, and themotion signal processing component 128 described above with reference toFIG. 1 are integrated into the sensor interface 114.

The components of the therapy delivery interface 116 couple one or moretherapy delivery devices, such as capacitors, defibrillator electrodeassemblies, pacing electrode assemblies, or mechanical chest compressiondevices, to the processor 118. It is appreciated that the functionalityof the therapy delivery interface 116 may be incorporated into thesensor interface 114 to form a single interface coupled to the processor118. In addition, the components of the network interface 106 couple theprocessor 118 to a computer network via a networking device, such as abridge, router or hub. According to a variety of examples, the networkinterface 106 supports a variety of standards and protocols, examples ofwhich include USB (via, for example, a dongle to a computer), TCP/IP,Ethernet, Wireless Ethernet, Bluetooth®, ZigBee, M-Bus, CAN-bus, IP,IPV6, UDP, DTN, HTTP, FTP, SNMP, CDMA, NMEA and GSM. It is appreciatedthat the network interface 106 of medical device controller 102 mayenable communication between other medical device controllers within acertain range.

To ensure data transfer is secure, in some examples, the medical devicecontroller 102 can transmit data via the network interface 106 using avariety of security measures including, for example, TLS, SSL, or VPN.In some examples, the network interface 106 includes both a physicalinterface configured for wireless communication and a physical interfaceconfigured for wired communication. According to various examples, thenetwork interface 106 enables communication between the medical devicecontroller 102 and a variety of personal electronic devices including,for example, computer enabled glasses, watches, and earpieces. In theseexamples, the network interface 106 may connect to and communicatethrough a body area network.

Thus, the various system interfaces incorporated in the medical devicecontroller 102 allow the device to interoperate with a wide variety ofdevices in various contexts. For instance, some examples of the medicaldevice controller 102 are configured to perform a process of sendingcritical events and data to a centralized server via the networkinterface 106. An illustration of a process in accord with theseexamples is disclosed in U.S. Pat. No. 6,681,003, titled “DATACOLLECTION AND SYSTEM MANAGEMENT FOR SUBJECT-WORN MEDICAL DEVICES,”issued on Jan. 20, 2004, which is hereby incorporated by reference inits entirety.

As illustrated in FIG. 1, the therapy delivery interface 116 and thenetwork interface 106 are optional and may not be included in everyexample. For instance, a heart rate monitor may employ the medicaldevice controller 102 to issue alarms but may not include a therapydelivery interface 116 to treat cardiopulmonary abnormalities.Similarly, an ambulatory defibrillator may include the medical devicecontroller 102 to provide defibrillation functionality but may notinclude a network interface 106 where, for example, the ambulatorydefibrillator is designed to rely on the user interface 108 to announcealarms.

The user interface 108 shown in FIG. 1 includes a combination ofhardware and software components that allow the medical devicecontroller 102 to communicate with an external entity, such as a subjector other user. These components may be configured to receive informationfrom actions such as physical movement, verbal intonation or thoughtprocesses. In addition, the components of the user interface 108 canprovide information to external entities. Examples of the componentsthat may be employed within the user interface 108 include keyboards,mouse devices, trackballs, microphones, electrodes, touch screens,printing devices, display screens, and speakers. In some examples, theelectrodes include an illuminating element, such as an LED. In someexamples, the printing devices include printers capable of renderingvisual or tactile (Braille) output.

Other examples may include a variety of features not shown in FIG. 1.For example, although the accelerometer/acoustic sensor 122 and theelectrode 120 are shown in FIG. 1 as discrete sensors, some examples mayintegrate the accelerometer/acoustic sensor 122 and the electrode 120into a single assembly. In other examples, the accelerometer/acousticsensor 122 is integrated within a therapy electrode assembly. One sucharrangement is described further in U.S. Patent Application PublicationNo. 2015/0005588, titled “THERAPEUTIC DEVICE INCLUDING ACOUSTIC SENSOR,”published Jan. 1, 2015, which is hereby incorporated herein by referencein its entirety. In other examples, the accelerometer/acoustic sensor122 is integrated within a garment such as the garment described furtherbelow with reference to FIG. 2. For instance, the accelerometer/acousticsensor 122 may be integrated within a belt, vest, or harness, such asthe harness 210 or the belt 250. Thus the examples disclosed herein arenot limited to a particular number or arrangement ofaccelerometer/acoustic sensors or electrodes.

Example Ambulatory Medical Device

In some examples, the medical device 100 described above with referenceto FIG. 1 is a wearable defibrillator that includes a garment (e.g., avest or belt) that is worn by the subject. FIG. 2 illustrates a wearabledefibrillator 200 in accord with these examples. In at least oneexample, the wearable defibrillator 200 may be a LifeVest® wearablecardioverter defibrillator available from ZOLL Medical Corporation ofChelmsford, Mass. The wearable defibrillator 200 monitors the subject'sECG with sensing electrodes, monitors the subject heart sounds withacoustic sensors, detects life-threatening arrhythmias, records eventsof interest, and delivers therapy in the form of one or more pacingpulses or a defibrillating shock through the therapy electrodes iftreatment is necessary. As shown in FIG. 2, the wearable defibrillator200 includes a harness 210 having a pair of shoulder straps and a beltthat is worn about the torso of a subject. The wearable defibrillator200 includes a plurality of ECG sensing electrodes 120 that are attachedto the harness 210 at various positions about the subject's body andelectrically coupled to the sensor interface 114 of the medical devicecontroller 102 via a connection pod 220. The plurality of ECG sensingelectrodes 120 are coupled to the medical device controller 102 tomonitor the cardiac function of the subject and generally include ananterior/posterior pair of ECG sensing electrodes and a side/side pairof ECG sensing electrodes. The plurality of ECG sensing electrodes 120may incorporate any electrode system, including conventional stick-onadhesive electrodes, dry-sensing capacitive ECG electrodes, radiotransparent electrodes, segmented electrodes, or one or more long termwear electrodes that are configured to be continuously worn by a subjectfor extended periods (e.g., 3 or more days). One example of such a longterm wear electrode is described in U.S. Patent Application PublicationNo. 2013/0325096, titled “LONG TERM WEAR MULTIFUNCTION BIOMEDICALELECTRODE,” published Dec. 5, 2013, which is hereby incorporated hereinby reference in its entirety. Additional ECG sensing electrodes may beprovided, and the plurality of ECG sensing electrodes 120 may bedisposed at various locations about the subject's body.

The wearable defibrillator 200 also includes one or moreaccelerometer/acoustic sensors 122 that are attached to a belt 250 ofthe harness 210 at various positions about the subject's body andelectrically coupled to the sensor interface 114 of the medical devicecontroller 102 via the connection pod 220. The one or moreaccelerometer/acoustic sensors 122 are coupled to the medical devicecontroller 102 to monitor the cardiopulmonary function of the subjectand generally positioned on the surface of a subject's body in theprecordial area.

FIG. 6 illustrates several potential example locations, labeled P, A, B,and T where accelerometer/acoustic sensors 122 can be placed. In oneexample, the accelerometer/acoustic sensors 122 are positioned near thethird intercostal parasternal area. Additionally, table 1 lists primarysurface anatomy locations where the accelerometer/acoustic sensors 122are positioned to detect various cardiac features and anomaliesaccording to some examples.

TABLE 1 Primary Auscultation Location Anomaly or Feature Secondintercostal space at right sternal border Aortic Value Secondintercostal space at left sternal border Pulmonary Value Thirdintercostal space at left sternal border Erb's Point Fourth or fifthintercostal spaces at the left sternal Tricuspid Value border Fifthintercostal space at midclavicular line Mitral Value

In some examples, the accelerometer/acoustic sensors 122 are positionedat secondary posterior locations that correspond to the anteriorpositions described above. For instance, the accelerometer/acousticsensors 122 may be positioned at locations near the scapula such as theposterior auxiliary line (V7), midscapular location (V8), or paraspinallocation (V9). In one example, the accelerometer/acoustic sensors 122are positioned at a secondary surface location proximal to the atria.

Although not shown is FIG. 2, the wearable defibrillator 200 may includeadditional sensors, other than the plurality of ECG sensing electrodes120, capable of monitoring the physiological condition or activity ofthe subject. For example, sensors capable of measuring blood pressure(via, for example, video blood pressure detection), heart rate, heartsounds, thoracic impedance, pulse oxygen level (via, for example,reflectance-based pulse oximetry to determine oxygen concentration),respiration rate, and the activity level of the subject may also beprovided.

The wearable defibrillator 200 also includes a plurality of therapyelectrodes 214 that are electrically coupled to the medical devicecontroller 102 via the connection pod 220 and which are configured todeliver one or more therapeutic defibrillating shocks to the body of thesubject, if it is determined that such treatment is warranted. Eachtherapy electrode of the plurality of therapy electrodes may be housedin a therapy electrode assembly that further includes conductive geldisposed within one or more reservoirs. Prior to delivering therapy, thetherapy electrode assembly may dispense the conductive gel to improveconductivity between the therapy electrode and the body of the subject.The connection pod 220 electrically couples the plurality of ECG sensingelectrodes 120 and the plurality of therapy electrodes 214 to thetherapy delivery interface 116 of the medical device controller 102, andmay include electronic circuitry configured for this purpose. Theconnection pod 220 may also include other electronic circuitry, such asa motion sensor or accelerometer through which subject activity may bemonitored.

As shown in FIG. 2, the wearable defibrillator 200 also includes a userinterface pod 240 that is electrically coupled to, or integrated inwith, the user interface 108 of the medical device controller 102. Theuser interface pod 240 can be attached to the subject's clothing or tothe harness 210, for example, via a clip (not shown) that is attached toa portion of the interface pod 240. In some examples, the user interfacepod 240 may simply be held in a person's hand. In some examples, theuser interface pod 240 may communicate wirelessly with the userinterface 108 of the medical device controller 102, for example, using aBluetooth®, Wireless USB, ZigBee, Wireless Ethernet, GSM, or other typeof communication interface.

The user interface pod 240 includes a number of buttons by which thesubject, or a bystander can communicate with the medical devicecontroller 102, and a speaker by which the medical device controller 102may communicate with the subject or the bystander. For example, wherethe medical device controller 102 determines that the subject isexperiencing a cardiac arrhythmia, the medical device controller 102 mayissue an audible alarm via a speaker on the medical device controller102 or the user interface pod 240 alerting the subject and anybystanders to the subject's medical condition. The medical devicecontroller 102 may also instruct the subject to press and hold one ormore buttons on the user interface 108 of the medical device controller102 or on the user interface pod 240 to indicate that the subject isconscious, thereby instructing the medical device controller 102 towithhold the delivery of one or more therapeutic defibrillating shocks.If the subject does not respond, the device may infer that the subjectis unconscious, and proceed with the treatment sequence, culminating inthe delivery of one or more defibrillating shocks to the body of thesubject.

In one example, the functionality of the user interface pod 240 isintegrated into the housing of the medical device controller 102. FIGS.3A-B illustrate such an example of the medical device controller 102.The medical device controller 102 includes two response buttons 310 onopposing sides of the housing of the medical device controller 102. Asshown in FIGS. 3A-B, the response buttons 310 are recessed to reduce thelikelihood of accidental activation (e.g., a subject falling on theresponse button). The medical device controller 102 also includes, inthis example, a display screen 320 and a speaker to enable thecommunication of audible and visual stimuli to the subject. It isappreciated that the response buttons 310 do not have to be placed onopposing sides of the housing as illustrated in FIGS. 3A-B. The responsebuttons, for example, may be located adjacent to each other in thehousing the medical device controller 102. The adjacent placement of theresponse buttons may make it easier for individuals with smaller handsor less dexterity to engage the response buttons.

Example Automated Medical Device

In some examples, the medical device 100 described above with referenceto FIG. 1 is an automated external medical device (AED). AEDs are smallportable defibrillators that are capable of monitoring cardiac rhythms,determining when a defibrillating shock is necessary, and administeringthe defibrillating shock either automatically, or under the control of atrained rescuer (e.g., an EMT or other medically training personnel).The AED, in addition, may be configured to provide counseling to anoperator as to how to perform cardiac resuscitation (CPR). FIG. 4illustrates an AED 400. The AED 400 may be, for example, an AED Plus®automated external defibrillator available from ZOLL Medical Corporationof Chelmsford, Mass. As shown, the AED 400 includes a medical devicecontroller 102 and an electrode assembly 402.

The electrode assembly 402 includes one or more sensing electrodes 120(e.g., ECG sensors), one or more acoustic sensors 122, one or moretherapy electrodes 404 (e.g., defibrillation pads), a connector 406,wiring 408 electrically coupling the connector 406 to the one or moresensing electrodes 120, the one or more acoustic sensors, and the one ormore therapy electrodes 404. As shown in FIG. 4, the connector isconfigured to couple the electrode assembly 402 to the medical devicecontroller 102 and, more specifically, the one or more sensingelectrodes 120 and the one or more acoustic sensors 122 to the sensorinterface 114 and the one or more therapy electrodes to the therapydelivery interface 116.

The medical device controller 102 of the AED 400 is configured to detectthe cardiac rhythm of the subject using ECG and heart sounds data andprovide pacing and defibrillating shocks to the subject as appropriate.This process is similar to the process described with regard to medicaldevice controller 102 of the ambulatory medical device 200. The userinterface 108 of the AED 400 may include a variety of componentsconfigured to communicate with the operator including, but not limitedto, a display screen, a speaker, and one or more buttons. In thisexample, the AED 400 includes a display screen to display notificationsto an operator. The notifications may provide instructions to theoperator regarding the proper administration of CPR to the subject. Thenotifications on the display may be accompanied by audible alarms fromthe speaker to further assist the operator in administering CPR to thesubject.

In another example, the medical device controller 102 (and moreparticularly, the cardiopulmonary function analyzer 112 of FIG. 1) ofthe AED is configured to utilize processed acoustic data to guide anoperator through a CPR procedure. In this example, the cardiopulmonaryfunction analyzer 112 issues step by step instructions to the operatorand analyzes processed electrode data, processed acoustic data, andprocessed motion data representative of heart sounds, breath sounds, andphysical movement (e.g., chest compressions) to validate performance ofeach step by the operator. Further, where performance of a given step isinadequate (e.g., chest compressions are not deep enough), thecardiopulmonary function analyzer 112 may issue additional instructionsto help the operator adequately perform the given step.

Anomaly Detection Processes

As described above, various examples implement processes through which amedical device identifies and addresses cardiopulmonary anomalies usingenhanced cardiopulmonary data. FIG. 5 illustrates one suchidentification process 500 that utilizes enhanced cardiopulmonary datato identify cardiopulmonary anomaly.

In act 502, a medical device (e.g., the medical device 100 of FIG. 1)receives acoustic, electrode, and motion signals generated fromdetectable characteristics of physical activity involving the subject,such as motion of the subject's body and/or the subject'scardiopulmonary function. These signals may be received via one or moreelectrodes (e.g., the electrode 120 of FIG. 1), one or more acousticsensors, (e.g., the accelerometer/acoustic sensor 122 of FIG. 1), or oneor more motion sensors (e.g., the accelerometer/acoustic sensor 122 ofFIG. 1). In act 504, the medical device analyzes the received signalsusing an acoustic signal processing component (e.g., the acoustic signalprocessing component 124 of FIG. 1), an electrode signal processingcomponent (e.g., the electrode signal processing component 126 of FIG.1), a motion signal processing component (e.g., the motion signalprocessing component 128) and a cardiopulmonary function analyzer (e.g.,the cardiopulmonary function analyzer 112 of FIG. 1).

For instance, in some examples of the act 504, the acoustic signalprocessing component and/or the motion signal processing componentreceive one or more signals from the one or more acoustic sensors,partition the one or more signals into one or more frequency bands, andprovide the partitioned signals to the cardiopulmonary function analyzerfor further processing. In some examples, the frequency bands correspondto signals generated by certain subject body movements and/or motionsuch as chest movements during patient breathing (e.g., frequencies lessthan 3 hertz), chest compressions being performed on the subject (e.g.,frequencies between 3 and 20 hertz), heart sounds of the subject (e.g.,frequencies between 20 hertz and 150 hertz), and breath sounds of thesubject (e.g., frequencies between 300 to 1200 hertz).

The frequency bands recited are approximate and may vary as needed tosupply the cardiopulmonary function analyzer with frequency domaininformation required to infer information regarding subject activity.For example, in one implementation, an estimate of motion may beseparated from an estimate of acoustic sounds by partitioning thefrequency band into low frequency (e.g., less than 10 hertz may beclassified as indicative of patient motion such as breathing movementsand/or chest compressions) and high frequency (e.g., greater than 10hertz, may be classified as indicative as patient acoustic sounds).

In act 506, the cardiopulmonary function analyzer determines whether thesubject's cardiopulmonary activity is normal. In some examples, thecardiopulmonary function analyzer determines that the subject'scardiopulmonary activity is normal by analyzing processed electrode dataand processed acoustic data covering the same cardiac cycle. Morespecifically, in some examples, the cardiopulmonary function analyzercompares processed electrode data to preconfigured benchmark data forthe subject. This preconfigured benchmark data may include, for example,metrics such as heart rate, QRS duration, QRS axis, PR interval, QTinterval, etc. In these examples, the cardiopulmonary function analyzeralso, within the act 506, compares processed acoustic data topreconfigured benchmark data that may include, for example, EMAT, LVST,LDPT, and % EMAT. Where the processed electrode data and processedacoustic data substantially match the preconfigured benchmark data, thecardiopulmonary function analyzer determines that the subject'scardiopulmonary activity is normal.

If the subject's cardiopulmonary activity is normal, the process 500ends. In the event of a detected anomaly, the cardiopulmonary functionanalyzer attempts to identify and verify an anomaly in act 508. Forinstance, in some examples of the act 508, the cardiopulmonary functionanalyzer attempts to identify and verify VT (as distinct from SVT) asfollows. First, the cardiopulmonary function analyzer detects a patternwithin processed electrode data that indicates whether a subject isexperiencing VT. For example, VT can manifest as high frequencyventricular pulses (e.g., more than 100 bpm, or in a range of 110-250bpm). The processed electrode data may indicate one or more of anon-sustained VT (e.g., a VT that is less than 30 seconds duration), asustained VT, a monomorphic VT (ventricular beats having similar or sameconfiguration), a polymorphic VT (ventricular beats having changingconfigurations), or a biphasic VT (a VT having a QRS complex thatchanges from beat to beat). In some cases, VT can originate from an areathat suffered a previous injury (e.g., an earlier myocardial infarctionsite). As such, the cardiopulmonary function analyzer can consider asource of the VT and correlate the information with the subject'shistory. To verify a VT episode detected in the processed electrodedata, the cardiopulmonary function analyzer can analyze the processedacoustic data covering the same cardiac cycle as the processed electrodedata. For example, VT can have a lower S1 intensity or higher S1variability than supraventricular rhythm (2.6335 1.78 mV vs. 4.70±5.03mV and 0.45±0.24 vs. 0.21±0.11, respectively). In this regard, thecardiopulmonary function analyzer can determine whether S1 intensity isbelow a value of a first configurable parameter. In some examples, thecardiopulmonary analyzer can also determine whether S1 beat-to-beatvariability is above a value of a second configurable parameter. Wherethe S1 intensity is below the value of the first configurable parameteror the S1 variability is above the value of the second configurableparameter, the cardiopulmonary function analyzer may conclude that thedetected VT episode is actual and verifies the VT episode. Otherwise,the cardiopulmonary function analyzer may conclude the detected VTepisode is unverified (e.g., the anomaly may be SVT). For example, thefirst configuration parameter for the S1 intensity threshold can beconfigured to be in a range defined by 4.70 mV±5.03 mV. For example, thesecond configurable parameter for the S1 variability threshold can be ina range defined by 0.21±0.11. It should be understood that other valuesoutside these ranges are possible, and are not to be limited to theranges described here.

In some examples of the act 508, the cardiopulmonary function analyzercan identify PEA as follows. For example, the cardiopulmonary functionanalyzer analyzes processed electrode data and detects a pattern withinthe processed electrode data that indicates a subject is experiencingPEA. For example, the cardiopulmonary function analyzer may detect thatthe QRS duration of the subject is greater than the value of aconfigurable parameter (e.g., 180 milliseconds). To verify this detectedPEA, the cardiopulmonary function analyzer can analyze processedacoustic data covering the same cardiac cycle as the processed electrodedata. More specifically, the cardiopulmonary function analyzer candetermine, for example, that the EMAT within the cardiac cycle isgreater than the value of a configurable parameter (e.g., 150milliseconds), or that the processed acoustic data includes no heartsounds. Where the cardiopulmonary function analyzer identifies either ofthese patterns in the processed acoustic data, the cardiopulmonaryfunction analyzer can confirm the detected PEA diagnosis and verifiesthe PEA episode. Otherwise, the cardiopulmonary function analyzer canconclude that the detected PEA is unverified.

In some examples of the act 508, the cardiopulmonary function analyzercan identify asystole as follows. For example, the cardiopulmonaryfunction analyzer analyzes processed electrode data and detects apattern within the processed electrode data that indicates a subject isexperiencing asystole. For example, asystole can manifest as a flatlinein ECG data. To verify this detected asystole, the cardiopulmonaryfunction analyzer can analyze processed acoustic data covering the samecardiac cycle as the processed electrode data. For example, thecardiopulmonary function analyzer can determine whether the processedacoustic data includes no heart sounds. If no heart sounds are detected,the cardiopulmonary function analyzer can confirm that the detectedasystole episode is a correct diagnosis and verifies the asystoleepisode. Otherwise, the cardiopulmonary function analyzer can identifythe detected asystole as unverified. In some implementations, thecardiopulmonary function analyzer can use other information such as,motion data (e.g., chest rise and fall), respiratory movements, orrespiratory sounds to confirm the asystole diagnosis.

In some examples of the act 508, the cardiopulmonary function analyzercan identify pseudo-PEA as follows. For example, the cardiopulmonaryfunction analyzer can analyze processed electrode data and detect apattern within processed electrode data that indicates a subject isexperiencing pseudo-PEA. For example, the cardiopulmonary functionanalyzer may detect that the QRS duration of the subject is within arange associated with pseudo-PEA (e.g., 120-180 milliseconds). Thisrange may be specified by one or more configurable parameters. To verifythis detected pseudo-PEA, the cardiopulmonary function analyzer cananalyze processed acoustic data covering the same cardiac cycle as theprocessed electrode data. More specifically, the cardiopulmonaryfunction analyzer can determine, for example, that the EMAT within thecardiac cycle is within a range specified by one or more configurableparameters (e.g., 90-150 milliseconds). Where the cardiopulmonaryfunction analyzer identifies this pattern in the processed acousticdata, the cardiopulmonary function analyzer can confirm the detectedpseudo-PEA diagnosis and verify the pseudo-PEA episode. Otherwise, thecardiopulmonary function analyzer can conclude that the detectedpseudo-PEA is unverified.

In some examples of the act 508, the cardiopulmonary function analyzercan identify degenerative bradycardia/pseudo-PEA/PEA where thecardiopulmonary function analyzer detects a gradually degrading EMATwithin processed acoustic data over a period of time having a durationspecified by a configurable parameter. In at least one example, theperiod of time is a configurable parameter with a value set between 10minutes and 1 hour. Where the EMAT degrades at a rate specified by aconfigurable parameter, the cardiopulmonary function analyzer verifiesthe degenerative bradycardia/pseudo-PEA/PEA episode.

In some examples of the act 508, the cardiopulmonary function analyzercan identify respiratory arrest as follows. For example, thecardiopulmonary function analyzer can analyze EMAT data within processedacoustic data and detect a gradually degrading EMAT over a period oftime. For example, such a period of time can have a duration specifiedby a configurable parameter. Next the cardiopulmonary function analyzercan determine whether, within the period of time with a graduallydegrading EMAT, a gradually increasing time span between the apex ofchest rise and the nadir of chest fall is being exhibited by thesubject. In some examples, the cardiopulmonary function analyzerdetermines the time span between the apex of the chest rise and thenadir of chest fall by analyzing low frequency (e.g., 0.1 Hz) and highfrequency breath sounds. Where the time span transgresses a thresholdvalue defined by a configurable parameter (and thus indicates laboredbreathing), the cardiopulmonary function analyzer can confirm thedetected respiratory arrest diagnosis, and thereby verify the detectedrespiratory arrest.

In some examples of the act 508, the cardiopulmonary function analyzercan identifies sleep apnea by analyzing the strength of the S3 sound ofa subject. Where the cardiopulmonary analyzer determines that the S3sound has strength greater than a value specified by a configurableparameter (e.g., 5), the cardiopulmonary function analyzer can confirmthat sleep apnea diagnosis and an episode of sleep apnea is verified.

In some examples of the act 508, the cardiopulmonary function analyzercan identify ECG sensing electrode falloff in some situations (e.g.,when the processed electrode data indicates that the subject isundergoing asystole). For example, consider a scenario in which thecardiopulmonary function analyzer analyzes processed electrode data anddetects a pattern within the processed electrode data that indicates asubject is experiencing asystole. To verify this detected asystoleepisode, the cardiopulmonary function analyzer can analyze the processedacoustic data from the same time period as the time period in which thecardiopulmonary function analyzer analyzes the processed electrode data.If the processed acoustic data includes normal heart sounds, thecardiopulmonary function analyzer can conclude that the detectedasystole episode is unverified. The cardiopulmonary function analyzercan then alert the subject, a support person, or other external entity,about a potential electrode falloff, rather than initiate a routinerelating to treating the asystole condition.

In some examples, the cardiopulmonary function analyzer can furtheranalyze processed motion data from a same time period as a time periodin which the cardiopulmonary function analyzer analyzes the processedelectrode data. Where the processed motion data indicates a subject isdisplaying a level of activity greater than a value of a configurableparameter, the cardiopulmonary function analyzer can conclude that thedetected asystole episode is actually a verified electrode falloff.

If, within the act 508, the cardiopulmonary function analyzer identifiesand verifies an anomaly, the cardiopulmonary function analyzer proceedsto act 510. Otherwise, the cardiopulmonary function analyzer proceeds tothe act 514. In the act 514 the cardiopulmonary function analyzer storesa record of the detected abnormal cardiopulmonary activity, issues anotification indicating the abnormal activity to an external entity(e.g., user or remote system), and returns to the act 502.

In act 510, the cardiopulmonary function analyzer identifies a routineassociated with the identified anomaly by, for example, referring to aconfigurable cross-reference parameter stored in a data storage (e.g.,the data storage 104 of FIG. 1) that associates each detectable anomalywith a routine to address the anomaly. Next, within act 512, thecardiopulmonary function analyzer addresses the anomaly by executing theidentified routine. These routines may address anomalies by executing avariety of processes. In one example of the act 512, the cardiopulmonaryfunction analyzer addresses verified VT, verified asystole, and verifiedPEA by executing a defibrillation sequence, culminating in the deliveryof one or more defibrillating shocks to the body of the subject. In someexamples of the act 512, the cardiopulmonary function analyzer addressesECG sensor falloff by communicating an alarm to an external entity, suchas a subject, a caregiver, a technician, or a remote computer system. Inat least one example of the act 512, the cardiopulmonary functionanalyzer addresses verified bradycardia and verified pseudo-PEA byexecuting a pacing routine. In some examples of the act 512, thecardiopulmonary function analyzer addresses verified respiratory arrestby communicating an alarm to the subject or a caregiver. In someexamples the alarm may include a mild shock or high volume audio signalto stir the subject. In some examples of the act 512, thecardiopulmonary function analyzer addresses verified or unverified SVTby communicating an alarm to a caregiver. Examples of the types ofalarms communicated in the act 512 are described in U.S. PatentApplication Publication No. 2012/0293323, titled “SYSTEM AND METHOD FORADAPTING ALARMS IN A WEARABLE MEDICAL DEVICE,” filed Mar. 23, 2012,which is hereby incorporated herein by reference in its entirety.

After execution of the act 512, the process 500 ends. The process 500may execute repeatedly during operation of the medical device to monitorand potentially treat the subject as needed.

Process 500 depicts one particular sequence of acts in a particularexample. The acts included in this process may be performed by, orusing, one or more computer systems specially configured as discussedherein. Some acts are optional and, as such, may be omitted in accordwith one or more examples. Additionally, the order of acts can bealtered, or other acts can be added, without departing from the scope ofthe systems and methods discussed herein. Furthermore, as discussedabove, in at least one example, the acts are performed on a particular,specially configured machine, namely a medical device configuredaccording to the examples disclosed herein.

Having thus described several aspects of at least one example of thisdisclosure, it is to be appreciated various alterations, modifications,and improvements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the scope of thedisclosure. Accordingly, the foregoing description and drawings are byway of example only.

1. A medical device comprising: at least one electrocardiogram (ECG)electrode; at least one acoustic sensor; and at least one processorcoupled with the at least one acoustic sensor and the at least one ECGelectrode and configured to receive at least one acoustic signal fromthe at least one acoustic sensor, to receive at least one electricalsignal from the at least one ECG electrode, and to detect at least onecardiopulmonary anomaly using the at least one electrical signal and theat least one acoustic signal.
 2. The medical device of claim 1, whereinthe at least one processor is configured to detect the at least onecardiopulmonary anomaly at least in part by identifying the at least onecardiopulmonary anomaly using the at least one electrical signal andverifying the at least one cardiopulmonary anomaly using the at leastone acoustic signal.
 3. The medical device of claim 1, furthercomprising a garment, wherein the at least one ECG electrode and the atleast one acoustic sensor are integrated with the garment.
 4. Themedical device of claim 3, further comprising at least one therapyelectrode coupled with the at least one processor and integrated withthe garment.
 5. The medical device of claim 1, wherein the at least oneacoustic signal comprises at least one of S1, S2, S3, and S4.
 6. Themedical device of claim 4, wherein the at least one processor isconfigured to detect the at least one cardiopulmonary anomaly at leastin part by calculating, based on the at least one acoustic signal, atleast one of electromechanical activation time (EMAT), EMAT as apercentage of cardiac cycle time, systolic dysfunction index, and leftventricular systolic time.
 7. The medical device of claim 1, wherein theat least one cardiopulmonary anomaly comprises at least one ofsupraventricular tachycardia, ventricular tachycardia, pulselesselectrical activity, asystole, a heart murmur, sleep apnea, andrespiratory failure.
 8. The medical device of claim 1, wherein the atleast one processor is configured to detect disconnection of the atleast one ECG electrode based on the at least one acoustic signal. 9.The medical device of claim 1, further comprising at least one motionsensor coupled with the at least one processor, wherein the at leastprocessor is configured to receive at least one motion signal from theat least one motion sensor and to detect the at least onecardiopulmonary anomaly using the at least one electrical signal, the atleast one acoustic signal, and the at least one motion signal.
 10. Themedical device of claim 1, wherein the at least one acoustic sensorcomprises at least one motion sensor.
 11. The medical device of claim10, wherein the at least one acoustic sensor comprises at least oneaccelerometer.
 12. The medical device of claim 11, wherein the at leastprocessor is configured to receive at least one signal from the at leastone accelerometer and to partition the at least one signal into the atleast one acoustic signal and at least one motion signal.
 13. Themedical device of claim 12, wherein the at least one motion signalincludes frequencies less than approximately 10 hertz and the at leastone acoustic signal includes frequencies greater than approximately 10hertz.
 14. The medical device of claim 12, wherein the at least oneprocessor is configured to partition the at least one signal into afirst frequency band for subject motion, a second frequency band forchest compression motion, a third frequency band for heart sounds, and afourth frequency band for breath sounds.
 15. The medical device of claim14, wherein the first frequency band comprises frequencies less thanapproximately 3 hertz, the second frequency band comprises frequenciesbetween approximately 3 and 20 hertz, the third frequency band comprisesfrequencies between approximately 20 hertz and 150 hertz, and the fourthfrequency band comprises frequencies between approximately 300 to 1200hertz.
 16. The medical device of claim 1, wherein the medical devicecomprises an ambulatory medical device.
 17. A method of monitoring asubject using a wearable defibrillator comprising at least oneelectrocardiogram (ECG) electrode and at least one acoustic sensor; themethod comprising: receiving at least one acoustic signal from the atleast one acoustic sensor; receiving at least one electrical signal fromthe at least one ECG electrode; and detecting at least onecardiopulmonary anomaly using the at least one electrical signal and theat least one acoustic signal.
 18. The method of claim 17, whereinreceiving the at least one acoustic signal comprises receiving at leastone of S1, S2, S3, and S4.
 19. The method of claim 18, wherein detectingthe at least one cardiopulmonary anomaly comprises calculating, based onthe at least one acoustic signal, at least one of electromechanicalactivation time (EMAT), EMAT as a percentage of cardiac cycle time,systolic dysfunction index, and left ventricular systolic time.
 20. Themethod of claim 17, wherein detecting the at least one cardiopulmonaryanomaly comprises detecting at least one of supraventriculartachycardia, ventricular tachycardia, pulseless electrical activity,asystole, a heart murmur, sleep apnea, and respiratory failure.
 21. Themethod of claim 17, wherein the medical device further comprises atleast one motion sensor and the method further comprises: receiving atleast one motion signal from the at least one motion sensor; anddetecting the at least one cardiopulmonary anomaly using the at leastone electrical signal, the at least one acoustic signal, and the atleast one motion signal.
 22. A medical device comprising: memory; atleast one accelerometer; and at least one processor coupled with the atleast one accelerometer and the memory and configured to receive atleast one acoustic signal from the at least one accelerometer, toreceive at least one motion signal from the at least one accelerometer,and to execute a sleep test configured to store data descriptive of theat least one acoustic signal and the at least one motion signal in thememory.
 23. The medical device of claim 22, further comprising at leastone ECG electrode coupled with the at least one processor, wherein theat least one processor is configured to receive at least one electricalsignal from the at least one ECG electrode and the sleep test isconfigured to store data descriptive of the at least one electricalsignal in the memory.
 24. The medical device of claim 23, furthercomprising a garment, wherein the at least one ECG electrode and the atleast one accelerometer are integrated with the garment.
 25. The medicaldevice of claim 24, further comprising at least one therapy electrodecoupled with the at least one processor and integrated with the garment.26. The medical device of claim 22, wherein the at least one processoris configured to execute the sleep test at a configurable time.
 27. Themedical device of claim 22, further comprising at least one motionsensor distinct from the accelerometer and coupled with the at least oneprocessor, the at least one motion sensor being positioned on a wrist ofa subject, wherein the at least one processor is configured to receiveone or more motion signals from the at least one motion sensor and thesleep test is configured to store data descriptive of the one or moremotion signals in the memory.